Routes into a Career in AI

Routes into a Career in AI

It feels like Artificial Intelligence is in the news almost every day, with new advances being reported one after the other. It is also accepted by pretty much everyone that the field can only continue to grow from here. With that increasing role comes a huge range of available careers in AI, often commanding impressive salaries. So how easy is it to enter this field? How can someone carve out a career in Artificial Intelligence? Let’s have look at the basics to begin your career in AI.

What do I need for a career in AI?

Whether you want to end up as a research scientist or data scientist, a machine learning or big data engineer, there are some common requirements across the field. Any job in AI is going to require a candidate who can demonstrate skills in in STEM subjects (Science, Technology, Engineering, Maths). While a degree may not be strictly necessary (some large companies are openly seeking people without advanced degrees) it will certainly make finding jobs easier. In that sense, a fairly traditional route, such as an undergraduate degree in something like physics, engineering, or maths, will be an enormously helpful start. From there, a master’s degree in a more specialised field will set you well on your way.

No degree?

However, if you don’t have that background, or can’t see yourself going to university to pick up a degree, don’t give up hope! There is a huge range of online courses specialising in working with artificial intelligence and machine learning, starting at the great price of free (and plenty that are cheap, or often available on a sale). Browse through Udemy, Udacity and Cousera for some excellent options, and there are plenty of other sites after that. It’s hard work getting through an online course on your own, but it can be done. Again, if you don’t want to get a certificate or qualification of some kind, it doesn’t make a career impossible, but it will be harder. Having a qualification can be a shortcut for an employer to see if you get through the first round of application. The other thing that may be relevant is experience with coding (which we touch on below). If you are a programmer or coder already, this may be another way into the field – you can start with proficiency in a language useful to the industry, and build qualifications on top of that.

Show What you Know for a Career in AI

Regardless of where you are in your AI journey, you should be thinking about your portfolio of work. When you are starting out, this will include pretty basic projects that have been done by plenty of other people before. As you begin, though, no one is expecting you to write software that can do something unique. But being able to demonstrate that you have taken a problem, worked with machine learning and AI, and the come up with a workable solution – that will be invaluable to potential hirers. Building a simple image classification algorithm, for example, is a popular demonstration of abilities.  As with other software jobs, it can be helpful to have a visible presence online. If you are able to help someone online (even someone asking a question you learned only days ago), this can be used to show your abilities. Keep an eye on sites like stackoverflow.com for questions you can help with, as well as be helped.

Keep Learning

This is pretty standard advice for any career, and you will doubtless find the same words said on any ‘How to work in…’ articles. But for a career in AI, it is especially important. The industry is developing so fast, and things are changing so quickly, that you absolutely have to keep up to date with developments if you want to stay relevant. Keep reading, keep practicing new things, keep learning.

Coding

Unsurprisingly, writing software is a major part of AI work. Data engineers, working more on infrastructure and database, are more likely to work with languages like SQL and use software like Hadoop. If you are leaning more to the Data Science side, Python and R may be wise choices. If you have a familiarity with coding languages, or you enjoy working with some of them, use those languages as a starting point – if you love using SQL, becoming a data engineer might be just the route for you to take.

Take it away

So, there’s a lot to think about and lot to do. If it sounds overwhelming, remember that at the moment, many companies are struggling to fill the positions they have for want of candidates. It is a great time to enter the field, and once you are ready to start applying for positions, don’t forget to go for ones you think might a step or two above your abilities – the company might well be willing to take a chance on someone starting out on their journey into AI. Good luck with your career in AI!

 

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